Development of Knowledge Management Model for Developing the Internal Quality Assurance in Educational Opportunity Expansion Schools
Bibliographic record
Abstract
This research for: 1) to study the current situation and problem in KM, 2) to develop the KM Model, and 3) to evaluate the finding usage of the KM Model for developing the Internal Quality Assurance of Educational Opportunity Expansion Schools. There were 3 Phases of research implementation. Phase 1: the current situation and problem in KM, was studied. Phase 2: the KM Model, was constructed, investigated, revised. Phase 3: the findings of usage in the KM Model for developing the Internal Quality Assurance of Educational Opportunity Expansion Schools. The research instruments for data collection were: the Questionnaire, and the Interview Form. The statistic using for data analysis included the Mean, Standard Deviation, and Percentage. The research findings found that: 1) The current situation and problem, found that were 7 Steps of implementation in KM including: the goal setting, the role determination, the knowledge construction, the shared learning, the knowledge selection, and the conclusions in body of knowledge, and the problem of KM, in overall, was in “High” level. 2) The KM Model, found that were 7 Steps of implementation in KM including: the goal setting, the role determination, the knowledge construction, the shared learning, the knowledge selection, and the conclusions in body of knowledge, and the delimitation of 8 aspects of Internal Quality Assurance. The findings of investigation in the KM Model, by all of 7 experts, found that the Mean Value was in “High” level. 3) The findings of evaluation in usage of KM Model, found that the Mean Value of testing after implementation of KM (posttest), was higher than before implementation of KM (pretest) at .01 significant level. The administrators and teachers had satisfaction in of KM Model, after the usage, found that it was in “High” level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".